summary.partialCor

Display estimated partial correlation and associated p-values and confidence intevals.

methods

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers"
taught by the section of Biostatistics of the University of Copenhagen.
It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...).
Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom.
Normalized residuals are provided to assess model misspecification.
Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients.
Predictions can be computed conditional to covariates only or also to outcome values.

Brice Ozenne

LMMstar

Repeated Measurement Models for Discrete Times

Julie Forman

summary.partialCor function

<dl><dt>object</dt>
<dd>a <code>partialCor</code> object, output of <code>partialCor</code>.</dd>
<dt>digits</dt>
<dd>[integer,&gt;0] number of digits used to display numeric values.</dd>
<dt>detail</dt>
<dd>[integer,&gt;0] passed to <code>print.confint_lmm</code>. If above 0.5 also display when a back-transformation has been used.</dd>
<dt>...</dt>
<dd>other arguments are passed to <code>print.confint_lmm</code>.</dd></dl>

Arguments

Summary for partial correlation — summary.partialCor

<dl>

<dt>object</dt>
<dd>a <code>partialCor</code> object, output of <code>partialCor</code>.</dd>


<dt>digits</dt>
<dd>[integer,&gt;0] number of digits used to display numeric values.</dd>


<dt>detail</dt>
<dd>[integer,&gt;0] passed to <code>print.confint_lmm</code>. If above 0.5 also display when a back-transformation has been used.</dd>


<dt>...</dt>
<dd>other arguments are passed to <code>print.confint_lmm</code>.</dd>

</dl>

summary.partialCor: Summary for partial correlation

Description

Usage

Arguments